George W. Norton Presented at the SANREM Evaluation Panel meeting Blacksburg, VA April 2013

Objectives of Economic Impact Assessment Program on SANREM To identify: 1) Costs and benefits of CA practices 2) Optimal CA practices in target cropping systems, and optimal sequencing of CAPS elements 3) Broader economic and social impacts of CA adoption 4) Policy changes to encourage CAPS adoption

Key Questions for Economic Analysis and Impact Assessment Are specific CAPS in specific countries profitable in the short run? If profitable, what are the optimal CAPS in each setting? What are the potential and actual economic, social, and environmental impacts of wide scale CAPS adoption? What are constraints to CAPS adoption? What is the adoption of CAPS over time in each country? What policy changes might encourage CAPS adoption, especially if the CAPS are beneficial in the long run but not profitable in the short run?

Structure of Impact Assessment The Impact Assessment cross-cutting research program interacts with regional programs to develop standardized data sets for economic impact analyses of CAPS Economic analyses take place in both the impact assessment program and the regional programs

I. Collaboration with regional programs Regional programs conduct baseline surveys and summarize and compare available input cost and yield data on standard form Some regional programs have conducted (Lesotho) or are conducting (Mozambique, Ecuador, Uganda) analyses of factors affecting adoption of CA practices Some regional programs (Ecuador, Nepal) have assessed optimal CAPs

Ghana – Partial budget analysis for trials Costs (GH¢/ha) MaizeSoybeans CT+NPKNT+NPKCTNT Labour Purchased input Total variable cost Yield (ton/ha) Revenue(GH¢/ha) Gross margin(GH¢/ha) B/C ratio Returns to labour labour productivity(Kg/Mndys) Costs(GH¢/ha) Treatments CT+NPKNT-NPKNT+NPKNT+P Labour Tractor use Fertilizer Herbicide Total variable cost Yield (ton/ha) Revenue(GH¢/ha) Gross margin(GH¢/ha) B/C ratio Returns to labour labour productivity(Kg/Mndys) (a)Nyoli with conventional versus no-till (b) Busa-Tangzu with conventional versus no-till

Ghana – Partial budgets for trials Costs(GH¢/ha) TREATMENTS FLATTIED RIDGESTR + GRASS STRIPS Labour Fertilizer TVC Yield (ton/ha) Revenue(GH¢/ha) Gross margin(GH¢/ha) B/C ratio Returns to labour labour productivity(Kg/Mndys) (C) Nandom maize trials with flat, tied ridges, and tied ridges plus grass strips treatments

Mozambique Budgets Tete, Manica, and Sofala Provinces, Mozambique 2008 – 2011 Check, Basins, Jab planter Maize/cowpea rotations N = 638 farmers, 22 villages NPK/Urea (all plots) Herbicide on CA plots Jab planter Basins (“likoti”)

Average input and labor costs for maize: Mozambique (2008 – 2011) Labor costs Input costs

Maize plots, Mozambique (2008 – 2011, N = 632 farmers, 3 plots each farmer, 22 villages)

Net returns: conventional tillage treatments and CA planting technologies for maize, Mozambique, 2008 – 2011 (N = 631 farms) Net returns (USD ha -1 )ControlBasinJab planter Mean Std. Dev CV H 0 : distributions not different*---- Control0.07 (0.0776)0.12 (0.0002) *Kolmogorov-Smirnoff test; D-statistic (p-value)

Partial budget analyses: Lesotho Response analysis of cover crop/no-till combinations and optimal inputs Study of best systems: Collecting production information from Champion Farmers (n = 25) and other farmers (n = 21) (gross margins, management practices, gender of DM) Wheat/vetch rotation Residue management “yes/no” side-by-side comparisons

Field production survey: Butha Buthe, Lesotho Survey data Compared management of No till and Conventional fields 728 field-level observations Maize production (summer, 2010) 553 conventional/51 no-till (usable) Fertilizer (NPK), labor, maize production, labor/input costs, plot size, seeding rate, maize prices

Field level inputs and production (Lesotho, 2010) VariableMeanN N -----CA Conventional--- Field area (ha) Seed (kg) Fertilizer (kg) Hired labor (person days) Maize production (kg) *Bold entries are different at the 5% level (t test)

Field level labor use (Lesotho, 2010) Total laborLabor hired In Notes: * Significant at the 10% level (t test) **** * *

Field level labor use (Lesotho, 2010) MenWomen *** * * Notes: * Significant at the 10% level (t test)

Example: Linear programming analysis LTRA-7 (Ecuador) (Nguema MS Thesis) Developed production coefficients and constraints based on: Earlier survey of 286 farms Expert interviews with agronomists, economists, and soil scientists New farmer surveys with 45 participants from an upper watershed and 43 from a lower watershed

Results: Illangama Watershed Optimal solution for typical farm household 1180 square meters of: (potatoes, fallow land, fava beans, fallow land rotation; conventional tillage; no deviation ditches; no cover crop) 1078 square meters of: (potatoes, oats-vetch, barley, fava beans rotation; conventional tillage; no deviation ditches; removal of cover crop) 864 square meters of: (potatoes, oats-vetch, barley, oats-vetch rotation; conventional tillage; no deviation ditches; incorporation of cover crop) Total of 3122 Square meters of crops per farm household

Results: Illangama Watershed Net revenue per farm: $2280 Deviation ditches not profitable Conventional tillage more profitable than reduced tillage

Results: Alumbre Watershed Optimal solution per farm household: 8,286 meters of corn, oats-vetch, beans, oats-vetch rotation; reduced tillage; manual weeding; removal of cover crop Net revenue per farm: $7700 Reduced tillage more profitable than conventional tillage Incorporation of cover crop not profitable Soil quality constraint not included in the model, but can be in the future SANREM research captures soil carbon content Analysis would require soil quality data from at least three years

Aggregate Benefits of CAPS through Economic Surplus Analysis Countries: Lesotho, Ecuador and Nepal Crops: maize, beans, potatoes, millet, and cowpeas Data required: Input cost and yield data from the regional projects Supply and demand elasticity estimates Price and quantity data Adoption rates (projecting benefits for 1%, 3%, 5% adoption) Research costs

D S0S0 S1S1 Price Quantity 0 P0P0 P1P1 d a b c I0I0 I1I1 Q0Q0 Q1Q1 Economic Surplus Analysis

Results: Lesotho Positive economic benefits for No-till maize No benefit from Lekoti system No-till: Annual undiscounted benefit ranging from $1.5 million at 1% adoption to $7.5 million at 5% adoption rate Net present value of benefits minus costs over 12 years of adoption: $12.6 million (1% adoption) $29.8 million (3% maximum adoption) $37.9 million (5% maximum adoption)

Results: Ecuador Positive economic benefits for all 4 CA treatments for maize Benefits for 2 of the 4 CA treatments for beans No benefits for potatoes

Economic Benefits: Maize in Ecuador CA Treatment 1 (manual weeding, conv. tillage, remove cover crop) – Annual economic benefits of $15 million (1% adoption) to $78 million (5% adoption) and net present value (NPV) over 12 years of $139.9 million to $403.7 million CA Treatment 2 (herbicide, conv. tillage, till in cover crop) – Annual economic benefits of $16 million to $80 million and 12 year benefits of $142.5 million to $411.2 million CA Treatment 3 (manual weeding, reduced till., remove cover crop) -- Annual economic benefits of $20 million to $100 million and 12 year benefits of $179 million to $516 million CA Treatment 4 (herbicide, reduced tillage, till in cover crop) -- Annual economic benefits of $18 million to $94 million and 12 year benefits of $167 million to $484 million

Economic Benefits: Beans in Ecuador CA Treatment 1 (manual weeding, conv. tillage, remove cover crop) No benefit CA Treatment 2 (herbicide, conv. tillage, till in cover crop) – Annual benefits of $.2 million (1% adoption) to $.8 million (5% adoption and net present value (NPV) over 12 years of $.55 million to $3.3 million CA Treatment 3 (manual weeding, reduced till., remove cover crop) No benefit CA Treatment 4 (herbicide, reduced tillage, till in cover crop) – Annual benefits of $.1 million to $.4 million and 12 year benefits up to $1.15 million

Results: Nepal Positive economic benefits for CA treatment 1 (maize in season 1, cowpeas in season 2, with conventional tillage) No benefit for CA treatments 2 and 3 (maize in season 1, cowpeas/millet intercropped in season 2, with conventional or strip tillage) CA 1: Annual benefits ranging from $1.5 million at 1% adoption to $7.7 million at 5% adoption rate Net present value of benefits minus costs over 12 years of adoption: $13.1 million (1% adoption) $30.7 million (3% maximum adoption) $39.0 million (5% maximum adoption)

Conclusions Positive benefits from Conservation Agriculture in each region studied Results vary widely among treatments and crops: Some non-profitable economic outcomes, but for others: 10’s to 100’s of millions of $ of benefits over 12 years even with low levels of adoption in some settings Outcome of the analysis impacted significantly by assumptions on adoption rate; can only do “what-if’ scenarios at this stage

Additional sites and topics Haiti – 600 households surveyed in Haiti and economic analyses underway at Virginia Tech Cambodia and Philippines – Trial data collected and to be analyzed at NCA&T Kenya and Uganda – Budget data being gathered on trials in 4 sites; Analyses of factors affecting adoption of CAPs is underway in 2 theses Ecuador -- Analysis of factors affecting adoption of CAPs is underway in thesis Ghana – Analysis of data from 3 years of cost of production data underway at Kansas State

Future Research Market level analysis of economic benefits in: Bolivia, Cambodia, Mali, Mozambique, Uganda Linear Programming analysis for additional regional programs to determine optimal mix of CA treatments Policies that might encourage adoption of CAPS Evaluation of economic value of environmental benefits of CAPS once we have data on carbon, etc.

Acknowledgement This presentation was made possible through support provided by the Agriculture Office within the Bureau for Economic Growth, Agriculture, and Trade (EGAT) of the U.S. Agency for International Development, under the terms of the Integrated Pest Management Collaborative Research Support Program (IPM CRSP) (Award No. EPP-A ). The opinions expressed herein are those of the author and do not necessarily reflect the views of the U.S. Agency for International Development.

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